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Review
. 2017 Nov-Dec;14(6):469-481.
doi: 10.21873/cgp.20057.

Role of Melt Curve Analysis in Interpretation of Nutrigenomics' MicroRNA Expression Data

Affiliations
Review

Role of Melt Curve Analysis in Interpretation of Nutrigenomics' MicroRNA Expression Data

Farid E Ahmed et al. Cancer Genomics Proteomics. 2017 Nov-Dec.

Abstract

This article illustrates the importance of melt curve analysis (MCA) in interpretation of mild nutrogenomic micro(mi)RNA expression data, by measuring the magnitude of the expression of key miRNA molecules in stool of healthy human adults as molecular markers, following the intake of Pomegranate juice (PGJ), functional fermented sobya (FS), rich in potential probiotic lactobacilli, or their combination. Total small RNA was isolated from stool of 25 volunteers before and following a three-week dietary intervention trial. Expression of 88 miRNA genes was evaluated using Qiagen's 96 well plate RT2 miRNA qPCR arrays. Employing parallel coordinates plots, there was no observed significant separation for the gene expression (Cq) values, using Roche 480® PCR LightCycler instrument used in this study, and none of the miRNAs showed significant statistical expression after controlling for the false discovery rate. On the other hand, melting temperature profiles produced during PCR amplification run, found seven significant genes (miR-184, miR-203, miR-373, miR-124, miR-96, miR-373 and miR-301a), which separated candidate miRNAs that could function as novel molecular markers of relevance to oxidative stress and immunoglobulin function, for the intake of polyphenol (PP)-rich, functional fermented foods rich in lactobacilli (FS), or their combination. We elaborate on these data, and present a detailed review on use of melt curves for analyzing nutigenomic miRNA expression data, which initially appear to show no significant expressions, but are actually more subtle than this simplistic view, necessitating the understanding of the role of MCA for a comprehensive understanding of what the collective expression and MCA data collectively imply.

Keywords: Biomarkers; DNA; PCR; RNA; SNPs; fermented sobya; food; miRNA; nutrigenomics; pomegranate.

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Figures

Figure 1
Figure 1. A diagram illustrating the flow of the randomized controlled nutrigenomic trial.
Figure 2
Figure 2. Array lay out for Qiagen’s human cancer RT2 miRNA qPCR Plate Array MAH-102A.
Figure 3
Figure 3. A. Graph illustrating the relation between fluorescence and and temperature for labeled probe designed for a wild-type (Wt) sequence, homozygous Wt, heterozygous Wt/Mut, and homozygous mutant Mut/Mut combination. B. Graph of the first derivative of melting curve -df/dT that pinpoints the temperature of dissociation, defined as 50% dissociation, by formed peaks. Figure courtesy of Integrated DNA Technologies www.idtdna.com; Downey N. 2014 Interpreting melt curves: An indicator, not a diagnosis. Online Coralville, Integrated DNA Technologies. Accessed May, 26, 2017.
Figure 4
Figure 4. Melt curves from qPCR of the CFTR Cystic Fibrosis Transmembrane Conductance Regulatorgene. A. An amplicon from CFTR exon 17b reveals a single peak following melt curve analysis. B. An amplicon from CFTR exon 7 reveals two peaks. Figure courtesy of Integrated DNA Technologies www.idtdna.com; Downey N. 2014 Interpreting melt curves: An indicator, not a diagnosis. Online Coralville, Integrated DNA Technologies. Accessed May, 26, 2017.
Figure 5
Figure 5. A. Change in fluorescence with increasing temperature is measured; as the temperature is increased from 65˚C, the two strands of the amplicon separate to form a ssDNA, causing the fluorescent intercalating dye to dissociate from DNA and stop fluorescing. B. The shoulder in the curve between 80˚C and 85˚C suggests the presence of an intermediate state where the DNA is in both ds and ss configurations. Figure courtesy of Integrated DNA Technologies www.idtdna.com;Downey N. 2014 Interpreting melt curves: An indicator, not a diagnosis. Online Coralville, Integrated DNA Technologies. Accessed May, 26, 2017.
Figure 6
Figure 6. A graphical representation of the parallel plot coordinates of the studies miRNA genes. The genes were ordered using the p-values of a one way ANOVA based on groups. Genes with the smallest p-values are presented first. Figure 6a shows control genes. In Figures 6b, c, five miRNA genes show separation.

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